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Reseach Article
GFF Classifier for Detection of Diabetic Retinopathy in Retinal Images
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 134 - Number 14 |
| Year of Publication: 2016 |
| Authors: Amol Prataprao Bhatkar, G. U. Kharat |
10.5120/ijca2016908053
|
Amol Prataprao Bhatkar, G. U. Kharat . GFF Classifier for Detection of Diabetic Retinopathy in Retinal Images. International Journal of Computer Applications. 134, 14 ( January 2016), 5-9. DOI=10.5120/ijca2016908053
Abstract
The emerging situation in today’s world suggests diabetic retinopathy may be a major problem in the medical world. Diabetic retinopathy is dangerous because it cannot be identified in its earlier stages and leads to vision loss. Hence, detection of diabetic retinopathy in early stage is very much important. This paper focuses on Generalized Feed Forward Neural Network (GFFNN) to detect diabetic retinopathy in retinal images. In this paper the authors present the GFFNN as a classifier to classify retinal images as normal and abnormal. 64-point Discrete Cosine Transform (DCT) and 09 statistical parameters such as Entropy, Mean, Standard deviation, Average, Euler number, Contrast, Correlation, Energy and Homogeneity are extracted from fundus retinal images to form a feature vector. The feature vector is used to train and test the GFFNN. The training and cross validation recognition rate by the GFFNN are 100% and 95.45% respectively for detection of normal and abnormal retinal images.
References
- Meindert Niemeijer,”Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs”,IEEE Transactions on Medical Imaging vol.29,no.1, January 2010..
- D. Klein, B. E Klein, S. E Moss et al “The Wisconsin epidemiologic study of diabetic retinopathy VII.Diabetic non proliferative retinal lesions”, Br. J Ophthalmol, vol. 94, 1986.
- Michael Goldbaum,Saied Moezzi, et al., “Automated diagnosis and image understanding with object extraction, object classification, and differencing in retinal images”,. Br. J Ophthalmol, vol 83, august 1999.
- C.Sinthanayothin, J.Boyce,et al.,“Automated localisation of optic disc, fovea, and retinal blood vessels from digital colour fundus images”, Br. J Ophthalmol, vol. 83, august 1999.
- Anil K. Jain Michigan State University, Jianchang Mao IBM Almaden Research Centre.,” Artificial neural networks: A tutorial”,1996.
- S.JeraldJeba Kumar, Madheswaran, “Extraction of Blood Vascular Network for Development of an Automated Diabetic Retinopathy Screening System”, International Conference on Computer Technology and Development IEEE 10.1109/Icctd.2009.212, 2009.
- María García, Carmen Valverde, “Comparison of Logistic Regression and Neural Network Classifiers in the Detection of Hard Exudates in Retinal Images”, 35th Annual International Conference of the IEEE EMBS Osaka, Japan, 3 - 7 July, 2013.
- G.U.Kharat & S.V.Dudul,” Optimal Neural Network Classifier for Human Emotion Recognition from Facial Expression using Singular Value Decomposition (SVD)”, Internal journal of Engineering Research and Industrial Application(IJERIA),pp 155-166,vol I,No.04,(ISSN-09704-1518),2008.
- A.P.Bhatkar & G.U.Kharat,” Detection of Diabetic Retinopathy in Retinal Images using MLP classifier ”, 2015 IEEE International Symposium on Nanoelectronic and Information Systems, 978-1-4673-9692-9/15 $31.00 © 2015 IEEE DOI 10.1109/iNIS.2015.30.
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